OpenRaiser/Intern-Atlas huggingface dataset:
Towards Universal Solvers: Using PGD Attack in Active Learning to Increase Generalizability of Neural Operators as Knowledge Distillation from Numerical PDE Solvers
Venue: arXiv cs.LG 2025
Year: 2025

Nonlinear PDE solvers require fine space-time discretizations and local linearizations, leading to high memory cost and slow runtimes. Neural operators such as FNOs and DeepONets offer fast single-shot inference by learning function-to-function mappings and truncating high-frequency components, but they suffer from poor out-of-distribution (OOD) generalization, often failing on inputs outside the training distribution. We propose an adversarial teacher-student distillation framework in which a differentiable numerical solver supervises a compact neural operator while a PGD-style active sampling loop searches for worst-case inputs under smoothness and energy constraints to expand the training set. Using differentiable spectral solvers enables gradient-based adversarial search and stabilizes sample mining. Experiments on Burgers and Navier-Stokes systems demonstrate that adversarial distillation substantially improves OOD robustness while preserving the low parameter cost and fast inference of neural operators.
SHRUMS: Sensor Hallucination for Real-time Underwater Motion Planning with a Compact 3D Sonar
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous navigation in 3D is a fundamental problem for autonomy. Despite major advancements in terrestrial and aerial settings due to improved range sensors including LiDAR, compact sensors with similar capabilities for underwater robots have only recently become available, in the form of 3D sonars. This paper introduces a novel underwater 3D navigation pipeline, called SHRUMS (Sensor Hallucination for Robust Underwater Motion planning with 3D Sonar). To the best of the authors' knowledge, SHRUMS is the first underwater autonomous navigation stack to integrate a 3D sonar. The proposed pipeline exhibits strong robustness while operating in complex 3D environments in spite of extremely poor visibility conditions. To accommodate the intricacies of the novel sensor data stream while achieving real-time locally optimal performance, SHRUMS introduces the concept of hallucinating sensor measurements from non-existent sensors with convenient arbitrary parameters, tailored to application specific requirements. The proposed concepts are validated with real 3D sonar sensor data, utilizing real inputs in challenging settings and local maps constructed in real-time. Field deployments validating the proposed approach in full are planned in the very near future.
XGen-Q: An Explainable Domain-Adaptive LLM Framework with Retrieval-Augmented Generation for Software Security
Venue: arXiv cs.IR 2025
Year: 2025

Generative AI and large language models (LLMs) have shown strong capabilities in code understanding, but their use in cybersecurity, particularly for malware detection and analysis, remains limited. Existing detection systems often fail to generalize to obfuscated or previously unseen threats, underscoring the need for more adaptable and explainable models. To address this challenge, we introduce XGen-Q, a domain-adapted LLM built on the Qwen-Coder architecture and pretrained on a large-scale corpus of over one million malware samples, spanning both source and assembly code. XGen-Q uses a multi-stage prompt strategy combined with retrieval-augmented generation (RAG) to deliver reliable malware identification and detailed forensic reporting, even in the presence of complex code obfuscation. To further enhance generalization, we design a training pipeline that systematically exposes the model to diverse obfuscation patterns. Experimental results show that XGen-Q achieves significantly lower perplexity than competitive baselines and exhibits strong performance on novel malware samples, demonstrating the promise of LLM-based approaches for interpretable and robust malware analysis.
SocializeChat: A GPT-Based AAC Tool Grounded in Personal Memories to Support Social Communication
Venue: arXiv cs.HC 2025
Year: 2025

Elderly people with speech impairments often face challenges in engaging in meaningful social communication, particularly when using Augmentative and Alternative Communication (AAC) tools that primarily address basic needs. Moreover, effective chats often rely on personal memories, which is hard to extract and reuse. We introduce SocializeChat, an AAC tool that generates sentence suggestions by drawing on users' personal memory records. By incorporating topic preference and interpersonal closeness, the system reuses past experience and tailors suggestions to different social contexts and conversation partners. SocializeChat not only leverages past experiences to support interaction, but also treats conversations as opportunities to create new memories, fostering a dynamic cycle between memory and communication. A user study shows its potential to enhance the inclusivity and relevance of AAC-supported social interaction.
When Can We Trust LLMs in Mental Health? Large-Scale Benchmarks for Reliable LLM Evaluation
Venue: arXiv cs.CL 2025
Year: 2025

Evaluating Large Language Models (LLMs) for mental health support is challenging due to the emotionally and cognitively complex nature of therapeutic dialogue. Existing benchmarks are limited in scale, reliability, often relying on synthetic or social media data, and lack frameworks to assess when automated judges can be trusted. To address the need for large-scale dialogue datasets and judge reliability assessment, we introduce two benchmarks that provide a framework for generation and evaluation. MentalBench-100k consolidates 10,000 one-turn conversations from three real scenarios datasets, each paired with nine LLM-generated responses, yielding 100,000 response pairs. MentalAlign-70k}reframes evaluation by comparing four high-performing LLM judges with human experts across 70,000 ratings on seven attributes, grouped into Cognitive Support Score (CSS) and Affective Resonance Score (ARS). We then employ the Affective Cognitive Agreement Framework, a statistical methodology using intraclass correlation coefficients (ICC) with confidence intervals to quantify agreement, consistency, and bias between LLM judges and human experts. Our analysis reveals systematic inflation by LLM judges, strong reliability for cognitive attributes such as guidance and informativeness, reduced precision for empathy, and some unreliability in safety and relevance. Our contributions establish new methodological and empirical foundations for reliable, large-scale evaluation of LLMs in mental health. We release the benchmarks and codes at: https://github.com/abeerbadawi/MentalBench/
From Memorization to Generalization: Fine-Tuning Large Language Models for Biomedical Term-to-Identifier Normalization
Venue: arXiv cs.CL 2025
Year: 2025

Effective biomedical data integration depends on automated term normalization, the mapping of natural language biomedical terms to standardized identifiers. This linking of terms to identifiers is essential for semantic interoperability. Large language models (LLMs) show promise for this task but perform unevenly across terminologies. We evaluated both memorization (training-term performance) and generalization (validation-term performance) across multiple biomedical ontologies. Fine-tuning Llama 3.1 8B revealed marked differences by terminology. GO mappings showed strong memorization gains (up to 77% improvement in term-to-identifier accuracy), whereas HPO showed minimal improvement. Generalization occurred only for protein-gene (GENE) mappings (13.9% gain), while fine-tuning for HPO and GO yielded negligible transfer. Baseline accuracy varied by model scale, with GPT-4o outperforming both Llama variants for all terminologies. Embedding analyses showed tight semantic alignment between gene symbols and protein names but weak alignment between terms and identifiers for GO or HPO, consistent with limited lexicalization. Fine-tuning success depended on two interacting factors: identifier popularity and lexicalization. Popular identifiers were more likely encountered during pretraining, enhancing memorization. Lexicalized identifiers, such as gene symbols, enabled semantic generalization. By contrast, arbitrary identifiers in GO and HPO constrained models to rote learning. These findings provide a predictive framework for when fine-tuning enhances factual recall versus when it fails due to sparse or non-lexicalized identifiers.
From Unweighted to Weighted Dynamic Matching in Non-Bipartite Graphs: A Low-Loss Reduction
Venue: arXiv cs.DS 2025
Year: 2025

We study the approximate maximum weight matching (MWM) problem in a fully dynamic graph subject to edge insertions and deletions. We design meta-algorithms that reduce the problem to the unweighted approximate maximum cardinality matching (MCM) problem. Despite recent progress on bipartite graphs -- Bernstein-Dudeja-Langley (STOC 2021) and Bernstein-Chen-Dudeja-Langley-Sidford-Tu (SODA 2025) -- the only previous meta-algorithm that applied to non-bipartite graphs suffered a $\frac{1}{2}$ approximation loss (Stubbs-Williams, ITCS 2017). We significantly close the weighted-and-unweighted gap by showing the first low-loss reduction that transforms any fully dynamic $(1-\varepsilon)$-approximate MCM algorithm on bipartite graphs into a fully dynamic $(1-\varepsilon)$-approximate MWM algorithm on general (not necessarily bipartite) graphs, with only a $\mathrm{poly}(\log n/\varepsilon)$ overhead in the update time. Central to our approach is a new primal-dual framework that reduces the computation of an approximate MWM in general graphs to a sequence of approximate induced matching queries on an auxiliary bipartite extension. In addition, we give the first conditional lower bound on approximate partially dynamic matching with worst-case update time.
Complexity of Unambiguous Problems in $Σ^P_2$
Venue: arXiv cs.CC 2025
Year: 2025

The complexity class $\bf{Σ^P_2}$ comprises problems based on polynomial-time checkable binary relations $ϕ(x,y)$ in which we ask whether there exists $x$ such that for all $y$, $ϕ(x,y)$ holds. We let $\bf{UΣ^P_2}$ denote the subclass of unambiguous problems in $\bf{Σ^P_2}$, namely those whose yes-instances correspond with a unique choice of $x$. $\bf{UΣ^P_2}$ is unlikely to have complete problems, but we identify various syntactic subclasses associated with general properties of $ϕ$ that guarantee uniqueness. We use these to classify the complexity of problems arising in social choice and game theory, such as existence of (1) a dominating strategy in a game, (2) a Condorcet winner, (3) a strongly popular partition in hedonic games, and (4) a winner (source) in a tournament. We classify these problems, showing the first is $\bf{Δ^P_2}$-complete, the second and third are complete for a class we term $\bf{PCW}$ (Polynomial Condorcet Winner), and the fourth for a class we term $\bf{PTW}$ (Polynomial Tournament Winner). We define another unambiguous class, $\bf{PMA}$ (Polynomial Majority Argument), seemingly incomparable to $\bf{PTW}$ and $\bf{PCW}$. We show that with randomization, $\bf{PCW}$ and $\bf{PTW}$ coincide with $\bf{Δ^P_2}$, and $\bf{PMA}$ is contained in $\bf{Δ^P_2}$. Specifically, we prove: $\bf{Δ^P_2} \subseteq \bf{PCW} \subseteq \bf{PTW} \subseteq \bf{S^P_2}$, and $\bf{coNP} \subseteq \bf{PMA} \subseteq \bf{S^P_2}$ (and it is known that $\bf{S^P_2}\subseteq \bf{ZPP^{NP}} \subseteq \bf{Σ^P_2} \cap \bf{Π^P_2}$). We demonstrate that unambiguity can substantially reduce computational complexity by considering ambiguous variants of our problems, and showing they are $\bf{Σ^P_2}$-complete. Finally, we study the unambiguous problem of finding a weakly dominant strategy in a game, which seems not to lie in $\bf{Σ^P_2}$.
Docker-based CI/CD for Rocq/OCaml projects
Venue: arXiv cs.SE 2025
Year: 2025

This paper presents three closely-related software projects, namely: docker-coq, docker-coq-action, and docker-keeper. It aims at two objectives: provide a high-level description of the available features -- to foster the use of a Docker-based CI/CD for Rocq (formerly known as Coq) or OCaml projects -- and document the underlying requirements and the main design choices of these three DevOps tools -- to help their future maintainers.
Recursive decoding of binary rank Reed-Muller codes and Plotkin construction for matrix codes
Venue: arXiv cs.IT 2025
Year: 2025

In 2021, Augot, Couvreur, Lavauzelle and Neri introduced a new class of rank metric codes which can be regarded as rank metric counterparts of Reed-Muller codes. Given a finite Galois extension $\mathbb{L} / \mathbb{K}$, these codes are defined as some specific $\mathbb{L}$-subspaces of the twisted group algebra $\mathbb{L} [\textrm{G}]$. We investigate the decoding of such codes in the "binary" case, \emph{i.e.,} when $\textrm{G} = (\mathbb{Z}/2\mathbb{Z})^m$. Our approach takes its inspiration from the decoding of Hamming metric binary Reed-Muller codes using their recursive Plotkin "$(u ~|~ u+v)$" structure. If our recursive algorithm restricts to a specific subclass of rank metric Reed-Muller codes, its asymptotic complexity beats that of the recently proposed decoding algorithm for arbitrary rank metric Reed-Muller codes based on Dickson matrices. Also, this decoder is of completely different nature and leads a natural rank metric counterpart of the Plotkin construction. To illustrate this, we also propose a generic Plotkin-like construction for matrix rank metric codes with an associate decoder, which can be applied to any pair of codes equipped with an efficient decoder.
X-Ego: Acquiring Team-Level Tactical Situational Awareness via Cross-Egocentric Contrastive Video Representation Learning
Venue: arXiv cs.CV 2025
Year: 2025

Human team tactics emerge from each player's individual perspective and their ability to anticipate, interpret, and adapt to teammates' intentions. While advances in video understanding have improved the modeling of team interactions in sports, most existing work relies on third-person broadcast views and overlooks the synchronous, egocentric nature of multi-agent learning. We introduce X-Ego-CS, a benchmark dataset consisting of 124 hours of gameplay footage from 45 professional-level matches of the popular e-sports game Counter-Strike 2, designed to facilitate research on multi-agent decision-making in complex 3D environments. X-Ego-CS provides cross-egocentric video streams that synchronously capture all players' first-person perspectives along with state-action trajectories. Building on this resource, we propose Cross-Ego Contrastive Learning (CECL), which aligns teammates' egocentric visual streams to foster team-level tactical situational awareness from an individual's perspective. We evaluate CECL on a teammate-opponent location prediction task, demonstrating its effectiveness in enhancing an agent's ability to infer both teammate and opponent positions from a single first-person view using state-of-the-art video encoders. Together, X-Ego-CS and CECL establish a foundation for cross-egocentric multi-agent benchmarking in esports. More broadly, our work positions gameplay understanding as a testbed for multi-agent modeling and tactical learning, with implications for spatiotemporal reasoning and human-AI teaming in both virtual and real-world domains. Code and dataset are available at https://github.com/HATS-ICT/x-ego.
When Facts Change: Probing LLMs on Evolving Knowledge with evolveQA
Venue: arXiv cs.CL 2025
Year: 2025

LLMs often fail to handle temporal knowledge conflicts--contradictions arising when facts evolve over time within their training data. Existing studies evaluate this phenomenon through benchmarks built on structured knowledge bases like Wikidata, but they focus on widely-covered, easily-memorized popular entities and lack the dynamic structure needed to fairly evaluate LLMs with different knowledge cut-off dates. We introduce evolveQA, a benchmark specifically designed to evaluate LLMs on temporally evolving knowledge, constructed from 3 real-world, time-stamped corpora: AWS updates, Azure changes, and WHO disease outbreak reports. Our framework identifies naturally occurring knowledge evolution and generates questions with gold answers tailored to different LLM knowledge cut-off dates. Through extensive evaluation of 12 open and closed-source LLMs across 3 knowledge probing formats, we demonstrate significant performance drops of up to 31% on evolveQA compared to static knowledge questions.
Video Consistency Distance: Enhancing Temporal Consistency for Image-to-Video Generation via Reward-Based Fine-Tuning
Venue: arXiv cs.CV 2025
Year: 2025

Reward-based fine-tuning of video diffusion models is an effective approach to improve the quality of generated videos, as it can fine-tune models without requiring real-world video datasets. However, it can sometimes be limited to specific performances because conventional reward functions are mainly aimed at enhancing the quality across the whole generated video sequence, such as aesthetic appeal and overall consistency. Notably, the temporal consistency of the generated video often suffers when applying previous approaches to image-to-video (I2V) generation tasks. To address this limitation, we propose Video Consistency Distance (VCD), a novel metric designed to enhance temporal consistency, and fine-tune a model with the reward-based fine-tuning framework. To achieve coherent temporal consistency relative to a conditioning image, VCD is defined in the frequency space of video frame features to capture frame information effectively through frequency-domain analysis. Experimental results across multiple I2V datasets demonstrate that fine-tuning a video generation model with VCD significantly enhances temporal consistency without degrading other performance compared to the previous method.
Controllable Machine Unlearning via Gradient Pivoting
Venue: arXiv cs.LG 2025
Year: 2025

Machine unlearning (MU) aims to remove the influence of specific data from a trained model. However, approximate unlearning methods, often formulated as a single-objective optimization (SOO) problem, face a critical trade-off between unlearning efficacy and model fidelity. This leads to three primary challenges: the risk of over-forgetting, a lack of fine-grained control over the unlearning process, and the absence of metrics to holistically evaluate the trade-off. To address these issues, we reframe MU as a multi-objective optimization (MOO) problem. We then introduce a novel algorithm, Controllable Unlearning by Pivoting Gradient (CUP), which features a unique pivoting mechanism. Unlike traditional MOO methods that converge to a single solution, CUP's mechanism is designed to controllably navigate the entire Pareto frontier. This navigation is governed by a single intuitive hyperparameter, the `unlearning intensity', which allows for precise selection of a desired trade-off. To evaluate this capability, we adopt the hypervolume indicator, a metric that captures both the quality and diversity of the entire set of solutions an algorithm can generate. Our experimental results demonstrate that CUP produces a superior set of Pareto-optimal solutions, consistently outperforming existing methods across various vision tasks.
A Design Science Blueprint for an Orchestrated AI Assistant in Doctoral Supervision
Venue: arXiv cs.CY 2025
Year: 2025

This study presents a design science blueprint for an orchestrated AI assistant and co-pilot in doctoral supervision that acts as a socio-technical mediator. Design requirements are derived from Stakeholder Theory and bounded by Academic Integrity. We consolidated recent evidence on supervision gaps and student wellbeing, then mapped issues to adjacent large language model capabilities using a transparent severity-mitigability triage. The artefact assembles existing capabilities into one accountable agentic AI workflow that proposes retrieval-augmented generation and temporal knowledge graphs, as well as mixture-of-experts routing as a solution stack of technologies to address existing doctoral supervision pain points. Additionally, a student context store is proposed, which introduces behaviour patches that turn tacit guidance into auditable practice and student-set thresholds that trigger progress summaries, while keeping authorship and final judgement with people. We specify a student-initiated moderation loop in which assistant outputs are routed to a supervisor for review and patching, and we analyse a reconfigured stakeholder ecosystem that makes information explicit and accountable. Risks in such a system exist, and among others, include AI over-reliance and the potential for the illusion of learning, while guardrails are proposed. The contribution is an ex ante, literature-grounded design with workflow and governance rules that institutions can implement and trial across disciplines.
See, Think, Act: Online Shopper Behavior Simulation with VLM Agents
Venue: arXiv cs.CY 2025
Year: 2025

LLMs have recently demonstrated strong potential in simulating online shopper behavior. Prior work has improved action prediction by applying SFT on action traces with LLM-generated rationales, and by leveraging RL to further enhance reasoning capabilities. Despite these advances, current approaches rely on text-based inputs and overlook the essential role of visual perception in shaping human decision-making during web GUI interactions. In this paper, we investigate the integration of visual information, specifically webpage screenshots, into behavior simulation via VLMs, leveraging OPeRA dataset. By grounding agent decision-making in both textual and visual modalities, we aim to narrow the gap between synthetic agents and real-world users, thereby enabling more cognitively aligned simulations of online shopping behavior. Specifically, we employ SFT for joint action prediction and rationale generation, conditioning on the full interaction context, which comprises action history, past HTML observations, and the current webpage screenshot. To further enhance reasoning capabilities, we integrate RL with a hierarchical reward structure, scaled by a difficulty-aware factor that prioritizes challenging decision points. Empirically, our studies show that incorporating visual grounding yields substantial gains: the combination of text and image inputs improves exact match accuracy by more than 6% over text-only inputs. These results indicate that multi-modal grounding not only boosts predictive accuracy but also enhances simulation fidelity in visually complex environments, which captures nuances of human attention and decision-making that text-only agents often miss. Finally, we revisit the design space of behavior simulation frameworks, identify key methodological limitations, and propose future research directions toward building efficient and effective human behavior simulators.
Calibration and Discrimination Optimization Using Clusters of Learned Representation
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning models are essential for decision-making and risk assessment, requiring highly reliable predictions in terms of both discrimination and calibration. While calibration often receives less attention, it is crucial for critical decisions, such as those in clinical predictions. We introduce a novel calibration pipeline that leverages an ensemble of calibration functions trained on clusters of learned representations of the input samples to enhance overall calibration. This approach not only improves the calibration score of various methods from 82.28% up to 100% but also introduces a unique matching metric that ensures model selection optimizes both discrimination and calibration. Our generic scheme adapts to any underlying representation, clustering, calibration methods and metric, offering flexibility and superior performance across commonly used calibration methods.